CalFishTrack

Central Valley Enhanced

Acoustic Tagging Project

Hatchery-origin winter-run Chinook salmon

2017-2018 Season (PROVISIONAL DATA)

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1. Project Status

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Study is complete, all tags are no longer active. All times in Pacific Standard Time.

See tagging details below:

Release_time	Number_fish_released	Release_location	Release_rkm	Mean_length	Mean_weight
2018-03-01 17:50:00	361	Bonnyview_Rel	540.258	111.9	16.5
2018-03-13 18:00:00	239	Bonnyview_Rel	540.258	97.5	10.7

2. Real-time Fish Detections

Sacramento real-time receivers deployed 2018-02-01, data current as of 2025-04-22 08:00:00. All times in Pacific Standard Time.

setwd(paste(file.path(Sys.getenv("USERPROFILE"),"Desktop",fsep="\\"), "\\Real-time data massaging\\products", sep = ""))

library(cder)
library(reshape2)

detects_study <- read.csv("C:/Users/field/Desktop/Real-time data massaging/products/Study_detection_files/detects_Winter_H_2018.csv", stringsAsFactors = F)
detects_study$DateTime_PST <- as.POSIXct(detects_study$DateTime_PST, format = "%Y-%m-%d %H:%M:%S", "Etc/GMT+8")

tagcodes <- read.csv("qry_HexCodes.txt", stringsAsFactors = F)
tagcodes$RelDT <- as.POSIXct(tagcodes$RelDT, format = "%m/%d/%Y %I:%M:%S %p", tz = "Etc/GMT+8")

detects_study <- detects_study[detects_study$general_location == "TowerBridge",]
detects_study <- merge(detects_study,aggregate(list(first_detect = detects_study$DateTime_PST), by = list(TagCode= detects_study$TagCode), FUN = min))

detects_study$Day <- as.Date(detects_study$first_detect, "Etc/GMT+8")

detects_study <- merge(detects_study, tagcodes[,c("TagID_Hex", "RelDT", "StudyID", "tag_life")], by.x = "TagCode", by.y = "TagID_Hex")
  
starttime <- as.Date(min(detects_study$RelDT), "Etc/GMT+8")
endtime <- min(as.Date(c(Sys.time())), max(as.Date(detects_study$RelDT)+detects_study$tag_life))
wlk_flow <- cdec_query("COL", "20", "H", starttime, endtime+1)
wlk_flow$datetime <- as.Date(wlk_flow$DateTime)
wlk_flow_day <- aggregate(list(parameter_value = wlk_flow$Value), by = list(Day = wlk_flow$datetime), FUN = mean, na.rm = T)

daterange <- data.frame(Day = seq.Date(from = starttime, to = endtime, by = "day"))

rels <- unique(tagcodes[tagcodes$StudyID == unique(detects_study$StudyID), "RelDT"])
rel_num <- length(rels)
rels_no_detects <- as.character(rels[!(rels %in% unique(detects_study$RelDT))])

tagcount <- aggregate(list(unique_tags = detects_study$TagCode), by = list(Day = detects_study$Day, RelDT = detects_study$RelDT ), FUN = function(x){length(unique(x))})
tagcount1 <- reshape2::dcast(tagcount, Day ~ RelDT)
                  
daterange1 <- merge(daterange, tagcount1, all.x=T)

if(length(rels_no_detects)>0){
  for(i in rels_no_detects){
    daterange1 <- cbind(daterange1, x=NA)
    names(daterange1)[names(daterange1) == 'x'] <- paste(i)
  }
}

daterange2 <- merge(daterange1, wlk_flow_day, by = "Day", all.x = T)

rownames(daterange2) <- daterange2$Day
daterange2$Day <- NULL

par(mar=c(6, 5, 2, 5) + 0.1)
barp <- barplot(t(daterange2[,1:ncol(daterange2)-1]), plot = FALSE, beside = T)
barplot(t(daterange2[,1:ncol(daterange2)-1]), beside = T, col=rainbow(rel_num), 
        xlab = "", ylab = "Number of fish arrivals per day", 
        ylim = c(0,max(daterange2[,1:ncol(daterange2)-1], na.rm = T)*1.2), 
        las = 2, xlim=c(0,max(barp)+1), cex.lab = 1.5, yaxt = "n", xaxt = "n")#, 
        #legend.text = colnames(daterange2[,1:ncol(daterange2)-1]),
        #args.legend = list(x ='topright', bty='n', inset=c(-0.2,0)), title = "Release Group")
legend(x ='topleft', legend = colnames(daterange2[,1:ncol(daterange2)-1]), fill= rainbow(rel_num), horiz = T, title = "Release Group")
ybreaks <- if(max(daterange2[,1:ncol(daterange2)-1], na.rm = T) < 4) {max(daterange2[,1:ncol(daterange2)-1], na.rm = T)} else {5}
xbreaks <- if(ncol(barp) > 10) {seq(1, ncol(barp), 2)} else {1:ncol(barp)}
barpmeans <- colMeans(barp)
axis(1, at = barpmeans[xbreaks], labels = rownames(daterange2[xbreaks,]), las = 2)
axis(2, at = pretty(0:max(daterange2[,1:ncol(daterange2)-1], na.rm = T), ybreaks))

par(new=T)

plot(x = barpmeans, daterange2$parameter_value, yaxt = "n", xaxt = "n", ylab = "", xlab = "", col = "blue", type = "l", lwd=2, xlim=c(0,max(barp)+1), ylim = c(min(daterange2$parameter_value, na.rm = T), max(daterange2$parameter_value, na.rm=T)*1.1))#, ylab = "Returning adults", xlab= "Outmigration year", yaxt="n", col="red", pch=20)
axis(side = 4)#, labels = c(2000:2016), at = c(2000:2016))
mtext("Flow (cfs) at Colusa Bridge", side=4, line=3, cex=1.5, col="blue")

2.1 Detections at Tower Bridge (downtown Sacramento) versus Sacramento River flows at Colusa Bridge

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3. Survival Probability

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3.1 Minimum survival to Tower Bridge (using CJS survival model)

Release Group	Survival (%)	SE	95% lower C.I.	95% upper C.I.	Detection efficiency (%)
ALL	18.4	1.8	15.1	22.2	64.4
2018-03-01 17:50:00	19.1	2.2	15.1	23.9	NA
2018-03-13 18:00:00	17.3	3.0	12.2	23.9	NA

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4. Detection Statistics

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4.1 Detections for all release groups combined

general_location	First_arrival	Mean_arrival	Fish_count	Percent_arrived	rkm
TowerBridge	2018-03-08 01:12:56	2018-03-16 21:21:57	71	11.83	172.000
I80-50_Br	2018-03-07 17:58:24	2018-03-17 00:11:12	73	12.17	170.748
Georgiana_Slough1	2018-03-24 19:50:52	2018-03-29 08:25:08	16	2.67	119.208
Sac_BlwGeorgiana	2018-03-22 17:51:34	2018-03-31 20:14:42	26	4.33	119.058

4.2 Detections for 2018-03-01 17:50:00 release group

general_location	First_arrival	Mean_arrival	Fish_count	Percent_arrived	rkm
TowerBridge	2018-03-08 01:12:56	2018-03-14 18:23:01	47	13.02	172.000
I80-50_Br	2018-03-07 17:58:24	2018-03-15 07:40:52	55	15.24	170.748
Sac_BlwGeorgiana	2018-03-22 17:51:34	2018-03-31 19:26:52	6	1.66	119.058

4.3 Detections for 2018-03-13 18:00:00 release group

general_location	First_arrival	Mean_arrival	Fish_count	Percent_arrived	rkm
TowerBridge	2018-03-19 17:33:49	2018-03-27 16:47:45	24	10.04	172.000
I80-50_Br	2018-03-18 04:46:45	2018-03-25 14:40:00	18	7.53	170.748
Georgiana_Slough1	2018-03-24 19:50:52	2018-03-29 08:25:08	16	6.69	119.208
Sac_BlwGeorgiana	2018-03-22 22:36:55	2018-03-31 20:47:59	20	8.37	119.058